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Exposition and Execution of a Scaled Aprori in Multidimensional Dataset

Keywords: Data Mining , Association Rule , Quantitative multidimensional theorem , Apriori algorithm , Frequent Itemset , UBtree

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This research work proposes discovery of quantitative association rule in spatial data that is related only to objects what enables data to be stored in relational database management system .The traditional Apriori algorithm is a method that helps to minimize the number of candidate sets while generating association rules by evaluating quantitative information associated with each item .We have proposed an algorithm for Aprori-UB which uses multidimensional access method, UB-tree to generate better association rules with high support and confidence. In multidimensional databases, objects are indexed according to several or many independent attributes. However, this task cannot be effectively realized using many standalone indices and thus special indexing structures have been developed is last two decades. Common to all this structures is that they index vectors of values instead of indexing single values. The UB-tree represents one of the promising multidimensional index structures. Indexing and querying highdimensional databases is a challenge for current research since high-dimensional indexing is significantly influenced phenomenon called curse of dimensionality .The proposed method in the paper reduces the number of item sets generated and also improves the execution time of the algorithm. Any valued attribute will be treated as quantitative and will be used to derive the quantitative association rule which usually helps in making the rules efficient to handle all types of data.


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